Boundaries and Prototypes in Categorizing Direction

Projective terms such as left, right, front, back are conceptually interesting due to their flexibility of contextual usage and their central relevance to human spatial cognition. Their default acceptability areas are well known, with prototypical axes representing their most central usage and decreasing acceptability away from the axes. Previous research has shown these axes to be boundaries in certain non-linguistic tasks, indicating an inverse relationship between linguistic and non-linguistic direction concepts under specific circumstances. Given this striking mismatch, our study asks how such inverse non-linguistic concepts are represented in language, as well as how people describe their categorization. Our findings highlight two distinct grouping strategies reminiscent of theories of human categorization: prototype based or boundary based. These lead to different linguistic as well as non-linguistic patterns

Intuitive Direction Concepts

Abstract Experiments in this article test the hypothesis that formal direction models used in artificial intelligence correspond to intuitive direction concepts of humans. Cognitively adequate formal models of spatial relations are important for information retrieval tasks, cognitive robotics, and multiple spatial reasoning applications. We detail two experiments using two objects (airplanes) systematically located in relation to each other. Participants performed a grouping task to make their intuitive direction concepts explicit. The results reveal an important, so far insufficiently discussed aspect of cognitive direction concepts: Intuitive (natural) direction concepts do not follow a one-size-fits-all strategy. The behavioral data only forms a clear picture after participants' competing strategies are identified and separated into categories (groups) themselves. The results are important for researchers and designers of spatial formalisms as they demonstrate that modeling cognitive direction concepts formally requires a flexible approach to capture group differences

Fundamental cognitive concepts of space (and time) : Using cross-linguistic, crowdsourced data to cognitively calibrate modes of overlap

This article makes several contributions to research on fundamental spatial and temporal concepts: First, we set out to render the notion of fundamental concepts of space and time more precise. Second, we introduce an efficient approach for collecting behavioral data combining crowdsourcing technology, efficient experimental software tools, and an effective and comprehensive analysis methodology. Third, we present behavioral studies that allow for identifying and calibrating potential candidates of fundamental spatial concepts from a cognitive perspective. Fourth, one prominent topic in the area of spatio-temporal cognition is the influence of language on how humans conceptualize their dynamic spatial environments. We used the aforementioned framework to collect data not only from English speaking participants but also from native Chinese and Korean speakers. Our application domain are the modes of overlap proposed by Galton [13]. We are able to show that the originally proposed spatial relations of the region connection calculus and intersection models are capturing cognitively fundamental distinctions that humans make with respect to modes of overlap. While finer distinctions are formally possible, they should not be considered fundamental conceptualizations in either Chinese, Korean, or English. The results show that our framework allows for efficiently answering questions about fundamental concepts of space, time, and space-time essential for theories of spatial information

Geospatial big data and cartography : research challenges and opportunities for making maps that matter

Geospatial big data present a new set of challenges and opportunities for cartographic researchers in technical, methodological, and artistic realms. New computational and technical paradigms for cartography are accompanying the rise of geospatial big data. Additionally, the art and science of cartography needs to focus its contemporary efforts on work that connects to outside disciplines and is grounded in problems that are important to humankind and its sustainability. Following the development of position papers and a collaborative workshop to craft consensus around key topics, this article presents a new cartographic research agenda focused on making maps that matter using geospatial big data. This agenda provides both long-term challenges that require significant attention as well as short-term opportunities that we believe could be addressed in more concentrated studies.PostprintPeer reviewe